Cited by 19Health Literacy and Information AccessibilityMobile Health and mHealth ApplicationsText Readability and Simplification

A Smart Mobile App to Simplify Medical Documents and Improve Health Literacy: System Design and Feasibility Validation

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Hendawi, Rasha, Alian, Shadi, Li, Juan·2022·JMIR Formative Research

Published version of a preprint

Abstract

BACKGROUND: People with low health literacy experience more challenges in understanding instructions given by their health providers, following prescriptions, and understanding their health care system sufficiently to obtain the maximum benefits. People with insufficient health literacy have high risk of making medical mistakes, more chances of experiencing adverse drug effects, and inferior control of chronic diseases. OBJECTIVE: This study aims to design, develop, and evaluate a mobile health app, MediReader, to help individuals better understand complex medical materials and improve their health literacy. METHODS: MediReader is designed and implemented through several steps, which are as follows: measure and understand an individual's health literacy level; identify medical terminologies that the individual may not understand based on their health literacy; annotate and interpret the identified medical terminologies tailored to the individual's reading skill levels, with meanings defined in the appropriate external knowledge sources; evaluate MediReader using task-based user study and satisfaction surveys. RESULTS: On the basis of the comparison with a control group, user study results demonstrate that MediReader can improve users' understanding of medical documents. This improvement is particularly significant for users with low health literacy levels. The satisfaction survey showed that users are satisfied with the tool in general. CONCLUSIONS: MediReader provides an easy-to-use interface for users to read and understand medical documents. It can effectively identify medical terms that a user may not understand, and then, annotate and interpret them with appropriate meanings using languages that the user can understand. Experimental results demonstrate the feasibility of using this tool to improve an individual's understanding of medical materials.

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  • Animation, text or social media promotion, or apps help patients acquire health information more effectively.

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References 34 · Cited by 19

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  1. [1]Weiss B Hart G McGee D D'Estelle S Health status of illiterate adults: relation between literacy and health status among persons with low literacy skills J Am Board Fam Pract 1992 5 3 257 64 1580173 1580173
  2. [2]Institute of Medicine Health Literacy: A Prescription to End Confusion 2004 Washington, DC The National Academies Press 1 366 25009856
  3. [3]Scott TL Gazmararian JA Williams MV Baker DW Health literacy and preventive health care use among Medicare enrollees in a managed care organization Med Care 2002 05 40 5 395 404 10.1097/00005650-200205000-00005 11961474 11961474 · DOI
  4. [5]Cutilli CC Bennett IM Understanding the health literacy of America: results of the National Assessment of Adult Literacy Orthop Nurs 2009 28 1 27 33 10.1097/01.NOR.0000345852.22122.d6 19190475 00006416-200901000-00011 19190475 PMC2668931 · DOI
  5. [7]Derevianchenko N Lytovska O Diurba D Leshchyna I Impact of medical terminology on patients' comprehension of healthcare Georgian Med News 2018 11 284 159 63 30618411 30618411
  6. [8]Bodenreider O The Unified Medical Language System (UMLS): integrating biomedical terminology Nucleic Acids Res 2004 01 1 32 Database issue 267 70 10.1093/nar/gkh061 14681409 32/suppl_1/D267 PMC308795 14681409 · DOI · Paper page
  7. [9]Vrandečić D Krötzsch M Wikidata Commun ACM 2014 09 23 57 10 78 85 10.1145/2629489 · DOI
  8. [10]National Center for Biotechnology Information 2022-01-23 https://www.ncbi.nlm.nih.gov/
  9. [13]OMIM - NCBI 2022-01-23 https://www.ncbi.nlm.nih.gov/omim
  10. [14]Rosse C Mejino JL Modayur BR Jakobovits R Hinshaw KP Brinkley JF Motivation and organizational principles for anatomical knowledge representation: the digital anatomist symbolic knowledge base J Am Med Inform Assoc 1998 01 01 5 1 17 40 10.1136/jamia.1998.0050017 9452983 9452983 PMC61273 · DOI
  11. [18]Davis TC Long SW Jackson RH Mayeaux EJ George RB Murphy PW Crouch MA Rapid estimate of adult literacy in medicine: a shortened screening instrument Fam Med 1993 06 25 6 391 5 8349060 8349060
  12. [19]Parker RM Baker DW Williams MV Nurss JR The test of functional health literacy in adults J Gen Intern Med 1995 10 10 10 537 41 10.1007/bf02640361 8576769 · DOI
  13. [20]Weiss BD The newest vital sign: frequently asked questions Health Lit Res Pract 2018 07 2 3 125 7 10.3928/24748307-20180530-02 31294286 PMC6607836 31294286 · DOI
  14. [21]Snelbaker AJ Wilkinson GS Robertson GJ Glutting JJ Dorfman WI Hersen M Wide Range Achievement Test 4 Understanding Psychological Assessment 2001 New York Springer 259 74
  15. [22]Lalor JP Wu H Chen L Mazor KM Yu H ComprehENotes, an instrument to assess patient reading comprehension of electronic health record notes: development and validation J Med Internet Res 2018 04 25 20 4 e139 10.2196/jmir.9380 29695372 v20i4e139 29695372 PMC5943623 · DOI
  16. [23]Reckase MD Item response theory: parameter estimation techniques Appl Psychol Measur 2016 07 27 22 1 89 91 10.1177/01466216980221009 · DOI
  17. [24]Reise SP Item response theory The Encyclopedia of Clinical Psychology 2014 Hoboken, New Jersey, United States John Wiley & Sons, Inc 1 10
  18. [25]Eftimov T Seljak BK Korošec P A rule-based named-entity recognition method for knowledge extraction of evidence-based dietary recommendations PLoS One 2017 6 23 12 6 e0179488 10.1371/journal.pone.0179488 28644863 PONE-D-16-46189 28644863 PMC5482438 · DOI
  19. [26]Kou Z Cohen WW Murphy RF High-recall protein entity recognition using a dictionary Bioinformatics 2005 06 16 21 Suppl 1 Suppl 1 266 73 10.1093/bioinformatics/bti1006 15961466 21/suppl_1/i266 15961466 PMC2857312 · DOI
  20. [27]Lample G Ballesteros M Subramanian S Kawakami K Dyer C Neural architectures for named entity recognition Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies June, 2016 San Diego, California 260 70 10.18653/v1/n16-1030 · DOI · Paper page
  21. [28]Ma X Hovy E End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 2016 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) August, 2016 Berlin, Germany 1064 74 10.18653/v1/p16-1101 · DOI · Paper page
  22. [29]Mousa A Schuller B Contextual bidirectional long short-term memory recurrent neural network language models: a generative approach to sentiment analysis Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers 2017 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers April 2017 Valencia, Spain 1023 32 10.18653/V1/E17-1096 · DOI
  23. [30]O'Shea K Nash R An Introduction to Convolutional Neural Networks ArXiv 2015 10.48550/arxiv.1511.08458 · DOI
  24. [32]Yin Z Shen Y On the dimensionality of word embedding Advances in Neural Information Processing Systems 31 (NeurIPS 2018) 2018 2022-03-22 https://papers.nips.cc/paper/2018/hash/b534ba68236ba543ae44b22bd110a1d6-Abstract.html
  25. [33]Pennington J Socher R Manning C GloVe: Global vectors for word representation Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) October 2014 Doha, Qatar 1532 43 10.3115/v1/D14-1162 · DOI · Paper page
  26. [34]Mohan S Li D MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts Arvix Preprint posted online February 25, 2019
  27. [35]Heaton J Ian Goodfellow, Yoshua Bengio, and Aaron Courville: Deep learning Genet Program Evolvable Mach 2017 10 29 19 1-2 305 7 10.1007/S10710-017-9314-Z · DOI
  28. [36]Turki H Shafee T Hadj Taieb MA Aouicha MB Vrandečić D Das D Hamdi H Wikidata: a large-scale collaborative ontological medical database J Biomed Inform 2019 11 99 103292 10.1016/j.jbi.2019.103292 31557529 S1532-0464(19)30211-4 31557529 · DOI
  29. [37]Stevenson M Guo Y Gaizauskas R Martinez D Disambiguation of biomedical text using diverse sources of information BMC Bioinformatics 2008 11 19 9 S11 1 11 10.1186/1471-2105-9-s11-s7 19025693 PMC2586756 · DOI
  30. [38]Stevenson M Wilks Y Word-sense disambiguation The Oxford Handbook of Computational Linguistics 2012 Oxfordshire, UK Oxford University Press
  31. [39]Salton G Wong A Yang CS A vector space model for automatic indexing Commun ACM 1975 11 18 11 613 20 10.1145/361219.361220 · DOI · Paper page
  32. [41]Kim H Statistical notes for clinical researchers: nonparametric statistical methods: 1. Nonparametric methods for comparing two groups Restor Dent Endod 2014 08 39 3 235 9 10.5395/rde.2014.39.3.235 25110650 25110650 PMC4125590 · DOI
  33. [42]Aronson AR Effective mapping of biomedical text to the UMLS Metathesaurus: the MetaMap program Proc AMIA Symp 2001 17 21 11825149 D010001275 11825149 PMC2243666
  34. [43]Savova GK Masanz JJ Ogren PV Zheng J Sohn S Kipper-Schuler KC Chute CG Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications J Am Med Inform Assoc 2010 09 01 17 5 507 13 10.1136/jamia.2009.001560 20819853 17/5/507 20819853 PMC2995668 · DOI · Paper page
  35. [44]Jonquet C Shah NH Musen MA The open biomedical annotator Summit Transl Bioinform 2009 03 01 2009 56 60 21347171 21347171 PMC3041576
  36. [45]Nunes T Campos D Matos S Oliveira JL BeCAS: biomedical concept recognition services and visualization Bioinformatics 2013 08 01 29 15 1915 6 10.1093/bioinformatics/btt317 23736528 btt317 23736528 · DOI
  37. [46]Tanenblatt M Coden A Sominsky I The ConceptMapper approach to named entity recognition Proceedings of the International Conference on Language Resources and Evaluation 2010 International Conference on Language Resources and Evaluation May 17-23, 2010 Valletta, Malta

Authors
Hendawi, R., Alian, S., & Li, J.
Affiliations
North Dakota State University (US)
Corresponding author
Li, Juan
Venue
JMIR Formative Research
Published
2022-04-01
Volume
6
Issue
4
Pages
e35069
ISSN
2561-326X (ISSN-L)
OpenAlex
W4224288542
Cited by
19
References
34
Citations by year
2026: 5 · 2025: 3 · 2024: 3 · 2023: 5 · 2022: 3
FWCI
4.02
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • JMIR Formative Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • National Science Foundation (1722913)
  • Division of Information and Intelligent Systems (1722913)
Topics
  • Health Sciences › Health Professions › General Health Professions › Health Literacy and Information Accessibility
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Text Readability and Simplification
Keywords
health literacy, mobile health apps, medical documents, medical terminology comprehension, user satisfaction, chronic disease management

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